[D] How do I get a fundamental mathematical understanding of modern generative modeling methods
Diffusion models, GAN, VAE, normalizing flows, etc. I "understand" those methods from an algorithmic perspective, diffusions gradually denoise an image, VAE use an encoder decoder architecture to turn an image into a latent distribution etc.
But from a statistical modeling standpoint, I'm really struggling, when I read papers like DDPM, DDIM or Normalizing Flows, I kind of undestand the notation, but I barely understand the statistical modeling, and I wouldn't be able to produce such thing myself
I want to understand this, which resources should I use ?
Are books like Bishop and Murphy enough ? Which one is the best ?
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